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freeze reqs before I install vb2

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wassname
2022-11-23 15:47:57 +08:00
parent b0fbc10dfd
commit a3b73a42eb
18 changed files with 2458 additions and 2783 deletions
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experiments/configs/Stocks/96M2S.gin
+1 -1
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@@ -24,7 +24,7 @@ Checkpoint.patience = 7
deeptime3.layer_size = 32
deeptime3.inr_layers = 5
deeptime3.dropout = 0.3
deeptime3.base_learner = 'Ridge'
deeptime3.lrn = 'Ridge'
deeptime3.n_fourier_feats = 2048
deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
experiments/configs/hp_search/Stocks.gin
+3 -3
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@@ -5,9 +5,9 @@ build.variables_dict = {
# 'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
# 'ForecastDataset.horizon_len': [6, 12, 24, 48, 96, 192, 336, 720],
# 'ForecastDataset.features': ['m', 'h', 'd'],
'deeptime3.base_learner': ['Ridge', 'None', 'Transformer'],
'deeptime3.lrn': ['Ridge', 'None', 'Transformer'],
'deeptime3.inr': ['INR', 'INRPlus2'],
'deeptime3.encoder': ['inception', 'lstm', 'mlp', 'lstm2', 'transformer', 'transformer2', 'none'],
'deeptime3.enc': ['inception', 'lstm', 'mlp', 'lstm2', 'transformer', 'transformer2', 'none'],
# 'deeptime3.dropout': [0.0, 0.1, 0.3, 0.5,],
}
@@ -31,7 +31,7 @@ Checkpoint.patience = 7
deeptime3.layer_size = 256
deeptime3.inr_layers = 5
deeptime3.dropout = 0.1
deeptime3.base_learner = 'Ridge'
deeptime3.lrn = 'Ridge'
deeptime3.n_fourier_feats = 4096
deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
experiments/forecast.py
+2 -2
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@@ -34,7 +34,7 @@ class ForecastExperiment(Experiment):
test_set, test_loader = get_data(flag='test')
dim_size=train_set.data_x.shape[1]
seq_len = train_set[0][1].shape[0]
seq_len = train_set[0][0].shape[0]
model = get_model(model_type,
dim_size=dim_size,
seq_len=seq_len,
@@ -51,7 +51,7 @@ class ForecastExperiment(Experiment):
metrics = {'val': val_metrics, 'test': test_metrics}
# np.save(join(self.root, 'metrics.npy'), {'val': val_metrics, 'test': test_metrics})
metrics = serialize(metrics)
json.dump(metrics, open(join(self.root, 'metrics.npy'), 'w'))
json.dump(metrics, open(join(self.root, 'metrics.json'), 'w'))
val_metrics = {f'ValMetric/{k}': v for k, v in val_metrics.items()}
test_metrics = {f'TestMetric/{k}': v for k, v in test_metrics.items()}